Overview

Dataset statistics

Number of variables10
Number of observations3276
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory256.1 KiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

Hardness has unique valuesUnique
Solids has unique valuesUnique
Chloramines has unique valuesUnique
Conductivity has unique valuesUnique
Organic_carbon has unique valuesUnique
Turbidity has unique valuesUnique

Reproduction

Analysis started2023-03-19 10:18:30.652298
Analysis finished2023-03-19 10:18:51.608697
Duration20.96 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

ph
Real number (ℝ)

Distinct2786
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0807945
Minimum0
Maximum14
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:51.996384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6888534
Q16.2776726
median7.0807945
Q37.8700498
95-th percentile9.6169359
Maximum14
Range14
Interquartile range (IQR)1.5923771

Descriptive statistics

Standard deviation1.469956
Coefficient of variation (CV)0.2075976
Kurtosis1.3760898
Mean7.0807945
Median Absolute Deviation (MAD)0.79758342
Skewness0.027795882
Sum23196.683
Variance2.1607706
MonotonicityNot monotonic
2023-03-19T13:18:52.297390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.080794504 491
 
15.0%
8.55409697 1
 
< 0.1%
6.538084087 1
 
< 0.1%
5.91580675 1
 
< 0.1%
8.136497869 1
 
< 0.1%
6.493764175 1
 
< 0.1%
6.977405633 1
 
< 0.1%
5.489248055 1
 
< 0.1%
2.558102799 1
 
< 0.1%
7.312109304 1
 
< 0.1%
Other values (2776) 2776
84.7%
ValueCountFrequency (%)
0 1
< 0.1%
0.2274990502 1
< 0.1%
0.9755779898 1
< 0.1%
0.9899122129 1
< 0.1%
1.431781555 1
< 0.1%
1.757037115 1
< 0.1%
1.844538366 1
< 0.1%
1.985383359 1
< 0.1%
2.128531434 1
< 0.1%
2.376768076 1
< 0.1%
ValueCountFrequency (%)
14 1
< 0.1%
13.54124024 1
< 0.1%
13.34988856 1
< 0.1%
13.17540172 1
< 0.1%
12.24692807 1
< 0.1%
11.90773983 1
< 0.1%
11.89807803 1
< 0.1%
11.62114013 1
< 0.1%
11.56876797 1
< 0.1%
11.56316906 1
< 0.1%

Hardness
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.3695
Minimum47.432
Maximum323.124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:52.601983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum47.432
5-th percentile141.76328
Q1176.85054
median196.96763
Q3216.66746
95-th percentile249.60977
Maximum323.124
Range275.692
Interquartile range (IQR)39.816918

Descriptive statistics

Standard deviation32.879761
Coefficient of variation (CV)0.16743823
Kurtosis0.61577168
Mean196.3695
Median Absolute Deviation (MAD)19.844989
Skewness-0.039341705
Sum643306.47
Variance1081.0787
MonotonicityNot monotonic
2023-03-19T13:18:53.129197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204.8904555 1
 
< 0.1%
134.5602761 1
 
< 0.1%
170.1909123 1
 
< 0.1%
237.4610992 1
 
< 0.1%
171.2389255 1
 
< 0.1%
197.4281988 1
 
< 0.1%
195.7440741 1
 
< 0.1%
184.2318535 1
 
< 0.1%
187.8732835 1
 
< 0.1%
205.1505644 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
47.432 1
< 0.1%
73.49223369 1
< 0.1%
77.4595861 1
< 0.1%
81.71089527 1
< 0.1%
94.09130748 1
< 0.1%
94.81254522 1
< 0.1%
94.90897713 1
< 0.1%
97.2809086 1
< 0.1%
98.3679149 1
< 0.1%
98.45293051 1
< 0.1%
ValueCountFrequency (%)
323.124 1
< 0.1%
317.3381241 1
< 0.1%
311.3839565 1
< 0.1%
308.2538329 1
< 0.1%
307.7060241 1
< 0.1%
306.6274814 1
< 0.1%
304.2359121 1
< 0.1%
303.7026267 1
< 0.1%
300.2924758 1
< 0.1%
298.0986795 1
< 0.1%

Solids
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22014.093
Minimum320.94261
Maximum61227.196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:53.590256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum320.94261
5-th percentile9545.8126
Q115666.69
median20927.834
Q327332.762
95-th percentile38474.99
Maximum61227.196
Range60906.253
Interquartile range (IQR)11666.072

Descriptive statistics

Standard deviation8768.5708
Coefficient of variation (CV)0.39831625
Kurtosis0.44282609
Mean22014.093
Median Absolute Deviation (MAD)5809.4719
Skewness0.62163449
Sum72118167
Variance76887834
MonotonicityNot monotonic
2023-03-19T13:18:53.872025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20791.31898 1
 
< 0.1%
15979.33479 1
 
< 0.1%
37000.95567 1
 
< 0.1%
18736.1909 1
 
< 0.1%
12289.90092 1
 
< 0.1%
15979.06027 1
 
< 0.1%
12431.80311 1
 
< 0.1%
30031.83918 1
 
< 0.1%
29532.615 1
 
< 0.1%
19821.33837 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
320.9426113 1
< 0.1%
728.7508296 1
< 0.1%
1198.943699 1
< 0.1%
1351.906979 1
< 0.1%
1372.091043 1
< 0.1%
2552.962804 1
< 0.1%
2808.025756 1
< 0.1%
2835.303165 1
< 0.1%
2912.211247 1
< 0.1%
3413.081633 1
< 0.1%
ValueCountFrequency (%)
61227.19601 1
< 0.1%
56867.85924 1
< 0.1%
56488.67241 1
< 0.1%
56351.3963 1
< 0.1%
56320.58698 1
< 0.1%
55334.7028 1
< 0.1%
53735.89919 1
< 0.1%
52318.9173 1
< 0.1%
52060.2268 1
< 0.1%
51731.82055 1
< 0.1%

Chloramines
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1222768
Minimum0.352
Maximum13.127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:54.145876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.352
5-th percentile4.5030537
Q16.1274208
median7.130299
Q38.114887
95-th percentile9.7531005
Maximum13.127
Range12.775
Interquartile range (IQR)1.9874663

Descriptive statistics

Standard deviation1.5830849
Coefficient of variation (CV)0.22227231
Kurtosis0.58990117
Mean7.1222768
Median Absolute Deviation (MAD)0.99166134
Skewness-0.01209844
Sum23332.579
Variance2.5061578
MonotonicityNot monotonic
2023-03-19T13:18:54.441640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.300211873 1
 
< 0.1%
9.504361027 1
 
< 0.1%
6.217222542 1
 
< 0.1%
5.599870342 1
 
< 0.1%
10.78649982 1
 
< 0.1%
7.424944591 1
 
< 0.1%
6.6616162 1
 
< 0.1%
6.21530731 1
 
< 0.1%
7.981036899 1
 
< 0.1%
6.344963412 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
0.352 1
< 0.1%
0.5303512947 1
< 0.1%
1.390870905 1
< 0.1%
1.683992581 1
< 0.1%
1.920271449 1
< 0.1%
2.102690991 1
< 0.1%
2.386653494 1
< 0.1%
2.39798499 1
< 0.1%
2.456013596 1
< 0.1%
2.458609195 1
< 0.1%
ValueCountFrequency (%)
13.127 1
< 0.1%
13.04380611 1
< 0.1%
12.91218664 1
< 0.1%
12.65336202 1
< 0.1%
12.62689974 1
< 0.1%
12.58002649 1
< 0.1%
12.36328483 1
< 0.1%
12.27937418 1
< 0.1%
12.2463941 1
< 0.1%
12.22717528 1
< 0.1%

Sulfate
Real number (ℝ)

Distinct2496
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean333.77578
Minimum129
Maximum481.03064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:54.721843image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum129
5-th percentile274.60794
Q1317.09464
median333.77578
Q3350.38576
95-th percentile395.5541
Maximum481.03064
Range352.03064
Interquartile range (IQR)33.291119

Descriptive statistics

Standard deviation36.142612
Coefficient of variation (CV)0.10828411
Kurtosis1.7899647
Mean333.77578
Median Absolute Deviation (MAD)16.625652
Skewness-0.041184373
Sum1093449.4
Variance1306.2884
MonotonicityNot monotonic
2023-03-19T13:18:54.976827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
333.7757766 781
 
23.8%
280.7456229 1
 
< 0.1%
332.7445192 1
 
< 0.1%
391.9182286 1
 
< 0.1%
330.9053704 1
 
< 0.1%
402.3134271 1
 
< 0.1%
360.6978151 1
 
< 0.1%
336.0404518 1
 
< 0.1%
405.5273372 1
 
< 0.1%
346.0636768 1
 
< 0.1%
Other values (2486) 2486
75.9%
ValueCountFrequency (%)
129 1
< 0.1%
180.2067464 1
< 0.1%
182.3973702 1
< 0.1%
187.1707144 1
< 0.1%
187.4241309 1
< 0.1%
192.0335917 1
< 0.1%
203.4445208 1
< 0.1%
205.9350906 1
< 0.1%
206.2472294 1
< 0.1%
207.8904823 1
< 0.1%
ValueCountFrequency (%)
481.0306423 1
< 0.1%
476.5397173 1
< 0.1%
475.7374602 1
< 0.1%
462.474215 1
< 0.1%
460.107069 1
< 0.1%
458.4410723 1
< 0.1%
455.4512337 1
< 0.1%
450.9144544 1
< 0.1%
449.2676875 1
< 0.1%
447.4179624 1
< 0.1%

Conductivity
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426.20511
Minimum181.48375
Maximum753.34262
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:55.261318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum181.48375
5-th percentile300.10947
Q1365.73441
median421.88497
Q3481.7923
95-th percentile566.34932
Maximum753.34262
Range571.85887
Interquartile range (IQR)116.05789

Descriptive statistics

Standard deviation80.824064
Coefficient of variation (CV)0.18963654
Kurtosis-0.27709283
Mean426.20511
Median Absolute Deviation (MAD)57.887591
Skewness0.26449022
Sum1396247.9
Variance6532.5293
MonotonicityNot monotonic
2023-03-19T13:18:55.527204image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
564.3086542 1
 
< 0.1%
418.6420628 1
 
< 0.1%
517.5767619 1
 
< 0.1%
235.0422835 1
 
< 0.1%
501.5597252 1
 
< 0.1%
452.1872326 1
 
< 0.1%
367.8540248 1
 
< 0.1%
400.6118991 1
 
< 0.1%
469.1321169 1
 
< 0.1%
482.5957093 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
181.483754 1
< 0.1%
201.6197368 1
< 0.1%
210.319182 1
< 0.1%
217.3583296 1
< 0.1%
232.613624 1
< 0.1%
233.9079651 1
< 0.1%
235.0422835 1
< 0.1%
245.859632 1
< 0.1%
247.9180305 1
< 0.1%
251.0208987 1
< 0.1%
ValueCountFrequency (%)
753.3426196 1
< 0.1%
708.2263645 1
< 0.1%
695.369528 1
< 0.1%
674.4434759 1
< 0.1%
672.5569992 1
< 0.1%
669.7250862 1
< 0.1%
666.6906183 1
< 0.1%
660.2549463 1
< 0.1%
657.5704218 1
< 0.1%
656.9241278 1
< 0.1%

Organic_carbon
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.28497
Minimum2.2
Maximum28.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:55.795037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile8.8153617
Q112.065801
median14.218338
Q316.557652
95-th percentile19.637254
Maximum28.3
Range26.1
Interquartile range (IQR)4.4918502

Descriptive statistics

Standard deviation3.308162
Coefficient of variation (CV)0.2315834
Kurtosis0.044409307
Mean14.28497
Median Absolute Deviation (MAD)2.2322941
Skewness0.025532582
Sum46797.563
Variance10.943936
MonotonicityNot monotonic
2023-03-19T13:18:56.069762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.37978308 1
 
< 0.1%
12.89763545 1
 
< 0.1%
15.87176979 1
 
< 0.1%
11.545477 1
 
< 0.1%
12.28433352 1
 
< 0.1%
18.58495937 1
 
< 0.1%
21.30064694 1
 
< 0.1%
15.28878163 1
 
< 0.1%
16.1692117 1
 
< 0.1%
12.16473568 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
2.2 1
< 0.1%
4.371898608 1
< 0.1%
4.466771969 1
< 0.1%
4.473092264 1
< 0.1%
4.861631498 1
< 0.1%
4.902888068 1
< 0.1%
4.966861619 1
< 0.1%
5.051694615 1
< 0.1%
5.159380308 1
< 0.1%
5.188466455 1
< 0.1%
ValueCountFrequency (%)
28.3 1
< 0.1%
27.00670661 1
< 0.1%
24.75539237 1
< 0.1%
23.95245044 1
< 0.1%
23.91760126 1
< 0.1%
23.66766678 1
< 0.1%
23.60429797 1
< 0.1%
23.56964491 1
< 0.1%
23.51477377 1
< 0.1%
23.39951606 1
< 0.1%

Trihalomethanes
Real number (ℝ)

Distinct3115
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.396293
Minimum0.738
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:56.370880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.738
5-th percentile39.906235
Q156.647656
median66.396293
Q376.666609
95-th percentile91.744595
Maximum124
Range123.262
Interquartile range (IQR)20.018954

Descriptive statistics

Standard deviation15.769881
Coefficient of variation (CV)0.23751147
Kurtosis0.40710218
Mean66.396293
Median Absolute Deviation (MAD)10.048534
Skewness-0.08516102
Sum217514.26
Variance248.68916
MonotonicityNot monotonic
2023-03-19T13:18:56.655123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66.39629295 162
 
4.9%
86.99097046 1
 
< 0.1%
56.71550955 1
 
< 0.1%
77.73081437 1
 
< 0.1%
90.39489472 1
 
< 0.1%
37.78709664 1
 
< 0.1%
78.9255271 1
 
< 0.1%
89.47771837 1
 
< 0.1%
69.526718 1
 
< 0.1%
72.57395938 1
 
< 0.1%
Other values (3105) 3105
94.8%
ValueCountFrequency (%)
0.738 1
< 0.1%
8.175876384 1
< 0.1%
8.577012933 1
< 0.1%
14.34316145 1
< 0.1%
15.6848768 1
< 0.1%
16.2915046 1
< 0.1%
17.00068293 1
< 0.1%
17.52776496 1
< 0.1%
17.91572257 1
< 0.1%
18.01527236 1
< 0.1%
ValueCountFrequency (%)
124 1
< 0.1%
120.030077 1
< 0.1%
118.3572747 1
< 0.1%
116.1616216 1
< 0.1%
114.2086714 1
< 0.1%
114.0349457 1
< 0.1%
113.0488857 1
< 0.1%
112.622733 1
< 0.1%
112.4122104 1
< 0.1%
112.0610274 1
< 0.1%

Turbidity
Real number (ℝ)

Distinct3276
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9667862
Minimum1.45
Maximum6.739
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.7 KiB
2023-03-19T13:18:56.936091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile2.6842792
Q13.4397109
median3.9550276
Q34.5003198
95-th percentile5.2209245
Maximum6.739
Range5.289
Interquartile range (IQR)1.0606089

Descriptive statistics

Standard deviation0.78038241
Coefficient of variation (CV)0.19672913
Kurtosis-0.062800641
Mean3.9667862
Median Absolute Deviation (MAD)0.53029624
Skewness-0.0078166424
Sum12995.191
Variance0.6089967
MonotonicityNot monotonic
2023-03-19T13:18:57.226519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.963135381 1
 
< 0.1%
3.987012091 1
 
< 0.1%
4.066229364 1
 
< 0.1%
3.759326201 1
 
< 0.1%
4.876273 1
 
< 0.1%
5.143750122 1
 
< 0.1%
4.513200539 1
 
< 0.1%
4.20418585 1
 
< 0.1%
4.586748359 1
 
< 0.1%
4.910911021 1
 
< 0.1%
Other values (3266) 3266
99.7%
ValueCountFrequency (%)
1.45 1
< 0.1%
1.492206615 1
< 0.1%
1.496100943 1
< 0.1%
1.64151501 1
< 0.1%
1.659799385 1
< 0.1%
1.680554025 1
< 0.1%
1.687624505 1
< 0.1%
1.801326999 1
< 0.1%
1.81252894 1
< 0.1%
1.844371604 1
< 0.1%
ValueCountFrequency (%)
6.739 1
< 0.1%
6.494748556 1
< 0.1%
6.494249467 1
< 0.1%
6.389161009 1
< 0.1%
6.35743852 1
< 0.1%
6.307678472 1
< 0.1%
6.226580405 1
< 0.1%
6.204846359 1
< 0.1%
6.099631873 1
< 0.1%
6.083772354 1
< 0.1%

Potability
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.7 KiB
0
1998 
1
1278 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3276
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Length

2023-03-19T13:18:57.472725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T13:18:57.684906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Most occurring characters

ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3276
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3276
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3276
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1998
61.0%
1 1278
39.0%

Interactions

2023-03-19T13:18:48.850260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:31.453698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:33.760256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:35.753589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:37.770692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:39.838030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:42.123467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:44.451915image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:46.398291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:49.087125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:31.769720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:33.991531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:35.978691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:38.008614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:40.077765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:42.350091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:44.682984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:46.629281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:49.306078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:31.985657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:34.208274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:36.208253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:38.229456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:40.313207image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:42.577501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:44.895422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:46.859186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:49.524173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:32.220955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:34.441019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:36.423195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:38.468225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:40.542260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:42.819304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:45.111347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:47.082504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:49.742657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:32.482189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:34.665555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:36.658732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:38.691838image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:40.767649image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:43.054178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:45.328098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:47.313966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:49.961226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:32.872996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:34.883109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:36.890944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:38.922555image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:41.131660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:43.272755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:45.551991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:47.545473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:50.179936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:33.090575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:35.097527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:37.106266image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:39.142814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:41.368141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:43.492097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:45.777400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:47.910660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:50.375028image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:33.310887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:35.305842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:37.322815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:39.349618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:41.586196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:43.704752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:45.975839image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:48.391106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:50.604191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:33.541055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:35.534868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:37.551503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:39.620934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:41.905378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:43.940403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:46.189528image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-19T13:18:48.627552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-19T13:18:57.869134image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
ph1.0000.107-0.064-0.0370.0220.0140.0390.006-0.0470.067
Hardness0.1071.000-0.053-0.025-0.074-0.0330.003-0.010-0.0130.079
Solids-0.064-0.0531.000-0.055-0.1310.0210.018-0.0190.0280.025
Chloramines-0.037-0.025-0.0551.0000.027-0.017-0.0120.018-0.0080.077
Sulfate0.022-0.074-0.1310.0271.000-0.0170.012-0.020-0.0170.130
Conductivity0.014-0.0330.021-0.017-0.0171.0000.021-0.0040.0100.000
Organic_carbon0.0390.0030.018-0.0120.0120.0211.000-0.007-0.0250.015
Trihalomethanes0.006-0.010-0.0190.018-0.020-0.004-0.0071.000-0.0270.000
Turbidity-0.047-0.0130.028-0.008-0.0170.010-0.025-0.0271.0000.000
Potability0.0670.0790.0250.0770.1300.0000.0150.0000.0001.000

Missing values

2023-03-19T13:18:50.909613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-19T13:18:51.370637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
07.080795204.89045520791.3189817.300212368.516441564.30865410.37978386.9909702.9631350
13.716080129.42292118630.0578586.635246333.775777592.88535915.18001356.3290764.5006560
28.099124224.23625919909.5417329.275884333.775777418.60621316.86863766.4200933.0559340
38.316766214.37339422018.4174418.059332356.886136363.26651618.436524100.3416744.6287710
49.092223181.10150917978.9863396.546600310.135738398.41081311.55827931.9979934.0750750
55.584087188.31332428748.6877397.544869326.678363280.4679168.39973554.9178622.5597080
610.223862248.07173528749.7165447.513408393.663396283.65163413.78969584.6035562.6729890
78.635849203.36152313672.0917644.563009303.309771474.60764512.36381762.7983094.4014250
87.080795118.98857914285.5838547.804174268.646941389.37556612.70604953.9288463.5950170
911.180284227.23146925484.5084919.077200404.041635563.88548117.92780671.9766014.3705620
phHardnessSolidsChloraminesSulfateConductivityOrganic_carbonTrihalomethanesTurbidityPotability
32668.372910169.08705214622.7454947.547984333.775777464.52555211.08302738.4351514.9063581
32678.989900215.04735815921.4120186.297312312.931022390.4102319.89911555.0693044.6138431
32686.702547207.32108617246.9203477.708117304.510230329.26600216.21730328.8786013.4429831
326911.49101194.81254537188.8260229.263166258.930600439.89361816.17275541.5585014.3692641
32706.069616186.65904026138.7801917.747547345.700257415.88695512.06762060.4199213.6697121
32714.668102193.68173547580.9916037.166639359.948574526.42417113.89441966.6876954.4358211
32727.808856193.55321217329.8021608.061362333.775777392.44958019.90322566.3962932.7982431
32739.419510175.76264633155.5782187.350233333.775777432.04478311.03907069.8454003.2988751
32745.126763230.60375811983.8693766.303357333.775777402.88311311.16894677.4882134.7086581
32757.874671195.10229917404.1770617.509306333.775777327.45976016.14036878.6984462.3091491